Network Analysis of Translocated Takahe Populations to Identify Disease Surveillance Targets

Abstract

Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i total ) and the number of ties (t total ) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k i in ), out degree (k i out ), and betweenness (B i ). Key players were assigned according to the highest node measure of k i in , k i out , and B i per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final network in 2011. Likewise, the wild Murchison Mountains population was consistently the sink of the network. Other nodes, such as the offshore islands and the wildlife hospital, varied in importance over time. Common network descriptors and measures of centrality identified key locations for targeting disease surveillance. The visual representation of movements of animals in a population that this technique provides can aid decision makers when they evaluate translocation proposals or attempt to control a disease outbreak.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

“…Networks consist of nodes and edges which represent possible interactions (i.e., transmission opportunities) between nodes. While nodes in network models commonly represent individuals, nodes can correspond to subpopulations or larger geographic areas such as counties or states when modelling spatial heterogeneity (Buhnerkempe et al., ; Grange, Van Andel, French, & Gartrell, ; Maher et al., ). While networks are not inherently spatial, they may approach purely spatial networks, i.e., a lattice or grid, particularly for certain methods of network construction (e.g., trap grids for small mammals) (Davis, Abbasi, Shah, Telfer, & Begon, ).…”

Using contact networks to explore mechanisms of parasite transmission in wildlife

“…For readers less familiar with network terminology, key network terms are italicized upon their first use and defined in the Appendix. Less commonly in models of spatial heterogeneity, nodes may represent larger geographic areas such as counties or states (Maher et al , ; Buhnerkempe et al , ; Grange et al , ). Thus, a ‘contact’ is any interaction that could potentially allow for transmission of an infectious agent between a pair of individuals, groups of individuals, or geographic regions.…”

Using a common commensal bacterium in endangered Takahe as a model to explore pathogen dynamics in isolated wildlife populations

“…Seven insurance populations (i.e., subpopulations external to the wild population created to mitigate deterministic and stochastic threats [Lee ]) and a breeding center have been established in predator‐free offshore and mainland reserves (Wickes et al ). The insurance populations are actively managed, through captive breeding and multiple translocations per annum, to minimize the potential detrimental effects of inbreeding (Jamieson et al ; Grange et al ). Subpopulations are geographically isolated and natural dispersal of Takahe is not possible.…”

“…Social network analysis of a Takahe translocation database identified a complex network of subpopulations that were predicted to vary in their likelihood of maintaining and transmitting pathogens (Grange et al ). Highly connected Takahe populations may have an important role in pathogen dispersal, whereas groups with fewer translocations could act as sinks or sources of exotic pathogens due to an increased possibility of allopatric speciation after a period of isolation (Grange et al ). The work by Grange et al () provides the basis for an empirical investigation into the molecular epidemiology of infectious organisms in the fragmented Takahe population.…”

“…Highly connected Takahe populations may have an important role in pathogen dispersal, whereas groups with fewer translocations could act as sinks or sources of exotic pathogens due to an increased possibility of allopatric speciation after a period of isolation (Grange et al ). The work by Grange et al () provides the basis for an empirical investigation into the molecular epidemiology of infectious organisms in the fragmented Takahe population. We sought to investigate the effects of population isolation, management, host biotic factors, and environmental variation on the carriage of enteric microorganisms.…”